Silicon Valley's Thomas Middleditch stars in a strangely dark but hilarious sci-fi movie written by an Artificial Intelligence (AI) that was created by filmmaker Oscar Sharp and his technologist collaborator Ross Goodwin. CBS News reports how the duo wanted to build a machine that could write screenplays, so they took hundreds of popular sci-fi TV and movie scripts and programmed them into an artificial intelligence Long Short-Term Memory (LTSM) neural network that they originally named Jetson. Ironically, Jetson asked to be addressed as Benjamin instead. "Sunspring" is the first movie written by artificial intelligence https://t.co/MhPA74XGm6 Benjamin is the first AI to write the script of a sci-fi film.

London – Experts from two US colleges have found the individual neuronal activity responsible for observational learning, a study published Tuesday in "Nature Communications" revealed. Researchers from the University of California - Los Angeles (UCLA) and the California Technical Insitute (Caltech) studied the activity of individual neurons with the aim of discovering the system through which the human brain learns from observing other individuals. According to research group leader Michael Hill, the study has managed to "transcend different levels of neuroscience," including the abstract level of computational models _ reflected in the activity of individual neurons _ and human behavior and interaction. The researchers studied brain activity by implanting chronic depth electrodes in ten patients suffering from epilepsy, who were then instructed to play a card game. During the game, information registered by the electrodes reflected changes in neuronal behavior caused by the process of learning through the observation of other players.

It must have seemed like a revolutionary idea to diners of the 1920s -- people on roller skates delivering food straight to their driver's side window. Suddenly, the food that families were used to eating around a dinner table now arrived on trays or in bags, ready to be eaten on the go. Little did they know fast food was on the brink of explosion. Nearly 100 years later, restaurants are on the brink of another massive change: robot automation. The best estimates find that up to 50% of jobs could be automated by the late 2030s, with restaurant workers among the most vulnerable to displacement.

In this guide we are going to discuss how to install and use the Datumbox Machine Learning framework in your Java projects. Since almost all of the code is written in Java, using it is as simple as including it as dependency in your Java project. Nevertheless a couple of classes (DataEnvelopmentAnalysis and LPSolver) use an external C library called lpsolve (Linear Programming Solver). Note that if you don't plan to use those 2 classes you are not required to install any binary libraries on your system. Nevertheless if you want to explore all the supported algorithms it is recommended to do the full installation as described below.

Gradient boosting involves the creation and addition of decision trees sequentially, each attempting to correct the mistakes of the learners that came before it. This raises the question as to how many trees (weak learners or estimators) to configure in your gradient boosting model and how big each tree should be. In this post you will discover how to design a systematic experiment to select the number and size of decision trees to use on your problem. How to Tune the Number and Size of Decision Trees with XGBoost in Python Photo by USFWSmidwest, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

As every tech company, big and small, races to make its app artificially intelligent by adding machine learning – software that teaches itself how to respond to stuff it's never seen before – the talent war to hire data scientists is on. There simply are not enough people with the big-data/machine-learning background to fill all the thousands of jobs that companies have open. So Salesforce's CEO, Marc Benioff, explained how he got the people he needed to build its major new machine-learning product, Einstein. Salesforce will launch Einstein next month at its massive annual Dreamforce tech conference. "Salesforce Einstein is AI for everyone. It's going to democratize artificial intelligence. It's going to make every company and every employee smarter, faster and more productive. We are going to deliver the world's smartest [customer relationship management]. "And as you know, over the last few years we have acquired a number of AI companies -- incredible companies like RelateIQ, MetaMind, Implisit, PredictionIO, Tempo AI, and more with amazing, amazing people and technology.

In H. G. Wells's classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn't react sooner to the arrival of an "intelligence greater than man's"--in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: "We will peck them to death tomorrow, my dear." As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should? To help you get the head start you may need, here are the signs that it's time to fly the nest.

Amazon.com Inc. AMZN 2.13 % recently poached an artificial-intelligence expert from rival eBay Inc., EBAY 0.40 % the latest move by the retail giant to strengthen its ability to quickly determine what users want. Hassan Sawaf joined Amazon as its director of artificial intelligence in Palo Alto, Calif., at its A9 labs, according to his LinkedIn page, with the goal of improving "the user experience for users of Amazon's search capability across all products and businesses." An eBay spokeswoman confirmed Mr. Sawaf left the company. Amazon and Mr. Sawaf didn't immediately respond to requests for comment. These companies and others are pouring money into artificial intelligence, which is expected to run everything from automobiles to houses in the future.

As humans cede more and more control to algorithms, whether in the courtroom or on social media, the way they are built becomes increasingly important. The foundation of machine learning is data gathered by humans, and without careful consideration, the machines learn the same biases of their creators. Sometimes bias is difficult to track, but other times it's clear as the nose on someone's face--like when it's a face the algorithm is trying to process and judge. An online beauty contest called Beauty.ai, run by Youth Laboratories (that lists big names in tech like Nvidia and Microsoft as "partners and supporters" on the contest website), solicited 600,000 entries by saying they would be graded by artificial intelligence. The algorithm would look at wrinkles, face symmetry, amount of pimples and blemishes, race, and perceived age. However, race seemed to play a larger role than intended; of the 44 winners, 36 were white.

It may be time to add'novelist' to the list of professions under threat from super-smart computer software, because a short story authored by artificial intelligence has made it through to the latter stages of a literary competition in Japan.